Abstract
A gender classification system uses a given image from human face to tell the gender of the given person. An effective gender classification approach is able to improve the performance of many other applications, including image or video retrieval, security monitoring, human-computer interaction and so on. In this paper, an effective method for gender classification task in frontal facial images based on convolutional neural networks (CNNs) is presented. Our experiments have been shown that the method of CNNs for gender classification task is effective and achieves higher classification accuracy than others on FERET and CAS-PEAL-R1 facial datasets. Finally, we built a gender classification demo, where input is the scene image per frame captured by the camera and the output is the original scene image with marked on detected facial areas.
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Acknowledgments
This research is partially supported by Beijing Natural Science Foundation (Grant 4152008). Hao is supported by the Foundation of Science and Technology of Beijing University of Technology (Grant ykj-2013-9341). We sincerely thank the anonymous reviewers for their thorough reviewing and valuable suggestions. In addition, the authors would also give warm thanks to Yutong Yu for the data labelling works, and Lei Wang and Zhiqiang Wang for their comments and discussions.
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Zhang, H., Zhu, Q., Jia, X. (2015). An Effective Method for Gender Classification with Convolutional Neural Networks. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham. https://doi.org/10.1007/978-3-319-27122-4_6
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DOI: https://doi.org/10.1007/978-3-319-27122-4_6
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